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Record W2799310976 · doi:10.1038/s41467-018-04345-y

Publisher Correction: Laser-accelerated particle beams for stress testing of materials

2018· erratum· en· W2799310976 on OpenAlexaff
M. Barberio, M. Scisciò, Simon Vallières, Fabio Cardelli, S. N. Chen, Gabriel Famulari, T. Gangolf, G. Revet, A. Schiavi, M. Senzacqua, P. Antici

Bibliographic record

VenueNature Communications · 2018
Typeerratum
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsMcGill UniversityInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBlankParticle (ecology)Stress (linguistics)Computer scienceInformation retrievalMaterials scienceComposite materialGeologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

The original version of the Supplementary Information associated with this Article contained an error in Supplementary Figure 3 in which all panels, with the exception of the bottom-left 'Ti' panel, were blank. The HTML has been updated to include a corrected version of the Supplementary Information.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.119
Threshold uncertainty score0.399

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.003
Science and technology studies0.0030.002
Scholarly communication0.0030.003
Open science0.0040.002
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.1190.067

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.291
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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